Sample Data
Five sample datasets (CT, MR, PET, NIfTI, and NRRD) are included with VoxelVision and automatically installed when the app is downloaded from the App Store. They are sourced from publicly available, de-identified research datasets and contain no personally identifiable information. Each dataset's source, license, and citation are detailed below.
Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., … Clarke, L. P. (2015). Data From LIDC-IDRI [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX
Armato, S. G., III, et al. (2011). The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38, 915–931. https://doi.org/10.1118/1.3528204
The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.
Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J. D., Flores Santamaria, N., Fathi Kazerooni, A., Pati, S., Rathore, S., Mamourian, E., Ha, S. M., Parker, W., Doshi, J., Baid, U., Bergman, M., Binder, Z. A., Verma, R., … Davatzikos, C. (2021). Multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System (UPENN-GBM) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.709X-DN49
Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., … Davatzikos, C. (2022). The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics. Scientific Data, 9(1), 453. https://doi.org/10.1038/s41597-022-01560-7
Reported research was partly supported by the National Cancer Institute (NCI), the National Institute of Neurological Disorders and Stroke (NINDS), and the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) under award numbers NINDS:R01NS042645, NCI:U24CA189523, NCI:U01CA242871, NCATS:UL1TR001878, and by the Institute for Translational Medicine and Therapeutics (ITMAT) of the University of Pennsylvania. The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH, or the ITMAT of the UPenn.
Kinahan, P., Muzi, M., Bialecki, B., Herman, B., & Coombs, L. (2019). Data from the ACRIN 6668 Trial NSCLC-FDG-PET (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.30ilqfcl
Machtay, M., Duan, F., Siegel, B. A., Snyder, B. S., Gorelick, J. J., Reddin, J. S., Munden, R., Johnson, D. W., Wilf, L. H., DeNittis, A., Sherwin, N., Cho, K. H., Kim, S., Videtic, G., Neumann, D. R., Komaki, R., Macapinlac, H., Bradley, J. D., & Alavi, A. (2013). Prediction of Survival by [18F]Fluorodeoxyglucose Positron Emission Tomography in Patients With Locally Advanced Non–Small-Cell Lung Cancer Undergoing Definitive Chemoradiation Therapy: Results of the ACRIN 6668/RTOG 0235 Trial. Journal of Clinical Oncology, 31(30), 3823–3830. https://doi.org/10.1200/jco.2012.47.5947
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7
Bilder, R., Poldrack, R., Cannon, T., London, E., Freimer, N., Congdon, E., Karlsgodt, K., & Sabb, F. (2020). UCLA Consortium for Neuropsychiatric Phenomics LA5c Study [Data set]. OpenNeuro. https://doi.org/10.18112/openneuro.ds000030.v1.0.0
Poldrack, R. A., Congdon, E., Triplett, W., Gorgolewski, K. J., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., Freimer, N. B., London, E. D., Cannon, T. D., & Bilder, R. M. (2016). A phenome-wide examination of neural and cognitive function. Scientific Data, 3, 160110. https://doi.org/10.1038/sdata.2016.110
This data was obtained from the OpenfMRI/OpenNeuro database (accession number ds000030). This work was supported by the Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083268, RL1MH083269, RL1DA024853, RL1MH083270, RL1LM009833, PL1MH083271, and PL1NS062410).
MRHead sample dataset, 3D Slicer SampleData. Donated to the 3D Slicer project by the individuals visible in the images, for use without restriction. https://www.slicer.org/
Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J-C., Pujol S., Bauer C., Jennings D., Fennessy F., Sonka M., Buatti J., Aylward S.R., Miller J.V., Pieper S., Kikinis R. (2012). 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magnetic Resonance Imaging, 30(9), 1323–1341. https://doi.org/10.1016/j.mri.2012.05.001
The MRHead dataset is provided through the 3D Slicer SampleData module and is used here with thanks to the 3D Slicer project and its contributors.
Validation References
Reference phantoms and datasets used in VoxelVision's geometric-validation study (manuscript in peer review). Analysis code, measurement records, and per-tier execution lists are available at github.com/mingweicui/VV-measurement-validation (MIT License).
The Phantom Laboratory, Incorporated. (2025). Catphan® 504 manual — product guide [Product guide]. The Phantom Laboratory. https://www.phantomlab.com/catphan-504
The external-reference scan of the CTP404 module is obtained from the publicly available pylinac demonstration dataset. Kerns, J. R. (2023). Pylinac: Image analysis for routine quality assurance in radiotherapy. Journal of Open Source Software, 8(92), 6001. https://doi.org/10.21105/joss.06001
Cui, M. (2026). MCVV phantom datasets (MCVV-SPH, MCVV-RPH): Synthetic and anatomy-like phantoms with geometric ground truth for volumetric medical-image measurement [Data set]. Zenodo. https://doi.org/10.5281/zenodo.21029537